Key Limitations:

The period of record is short for most of these data sets, and interannual variability and trends are poorly known

To account for differeing sensitivites and sampling biases, model output must be processed to estimate what the instruments would see if they measured the model's atmosphere.

The derived δD values have generally high uncertainties. Absolute values are not in agreement among the datasets, though each dataset shows realistic zonal gradients and seasonal cycles.

Expert User Guidance

#General Comments

Remote-sensing observations of the water vapor isotopic composition can be used as a complementary tool to evaluate the hydrological cycle in atmospheric models.

Collocation:

Whatever the dataset used to evaluate a model, to take into account the spatio-temporal sampling biases of the data, it is preferable to collocate the model output with each of the measurements at a scale daily or shorter. To ensure that the large-scale circulation in the model is consistent with that of the data on a day-to-day basis, it is preferable to use simulations whose winds are nudged by reanalyses.

Kernel convolution:

In addition to the collocation, to take into account the sensitivity of the instruments, the model outputs need to be convolved with averaging kernels available for some of the datasets.

Clear-sky sampling bias:

All datasets sample preferentially clear-sky scenes. Even when collocating model outputs from a nudged simulation, the model might simulate cloudy conditions when the data sees clear-sky and vice versa. Therefore, in the model the clear-sky sampling bias can be under-estimated compared to the data. This effect is difficult to take into account since the definition of a cloud varies between instruments and between models.

Specific to TES:

Quality selection:

We select only retrievals for which the degree of freedom for signal is

higher than 0.5

Bias correction:

When using the TES data, a correction must be applied which decreases the deltaD by about 4 permil. This correction depends on the averaging kernels of individual measurements, as described in Lee et al 2011 and Risi et al submitted.

Sensitivity:

The sensitivity of the TES deltaD retrievals decreases as surface temperature decreases, as humidity decreases and as cloud cover increases. The sensitivity becomes very small poleward of 45°S and 45°N. When the sensitivity is small, the retrieved deltaD tends toward a constant a-priori profile which is relatively enriched. Therefore, the TES retrievals underestimate the deltaD latitudinal gradient (Risi et al submitted).

Specific to SCIAMACHY:

Quality selection:

To avoid potential isotopic biases related to the presence of clouds or sampling of an incomplete atmospheric column, we discard all retrievals associated with a cloud fraction higher than 10% or with a retrieved precipitable water differing from ECMWF reanalyses by more than 10% (Risi et al 2010, Risi et al submitted).

Kernel convolution:

No averaging kernel are available. We just need to calculate the total column average deltaD from the model outputs.

Specific to ACE:

Quality selection:

We discarded measurements with errors in H2O and HDO higher than the retrieved values. However, this leads to a slight bias towards measurements when H2O. In addition, we apply a 3 median average deviation filter to remove outliers (Risi et al submitted).

Kernel convolution:

ACE does not use optimal estimation, and averaging kernels are not computed. To take into account the vertical resolution of the data, we convolved the model outputs with a triangular kernel of base 3 km (Dupuy et al 2008).

Sampling:

The sampling is very sparse. In the upper troposphere, the spatial coverage is insufficient to plot maps. Only zonal averages at best can be analyzed (Risi et al submitted).

Specific to MIPAS:

Quality selection:

We discard data with the visibility flag equal to zero and with diagonal elements of the averaging kernels lower than 0.03 (Risi et al submitted).

Kernel convolution:

Model outputs need to be convolved with averaging kernels and a-priori profiles for each measurement the model is being collocated with.

Alternatively, since averaging kernels depend mainly on the tropopause height, we can use pre-computed representative averaging kernels for different tropopause height (Risi et al submitted).

The National Center for Atmospheric Research is sponsored by the National Science Foundation. Any opinions, findings and conclusions or recommendations expressed in this material do not necessarily reflect the views of the National Science Foundation.